Indian monsoon is an important component of earth’s climate system. Daily rainfall data for longer period is vital to study components and processes related to Indian monsoon. Daily observed gridded rainfall data covering both land and adjoining oceanic regions are required for numerical model validation and model development for monsoon. In this study, a new gridded daily Indian rainfall dataset at 1° × 1° latitude/longitude resolution covering 14 monsoon seasons (1998–2011) are described. This merged satellite gauge rainfall dataset (NMSG) combines TRMM TMPA rainfall estimates with gauge information from IMD gridded data. Compared to TRMM and GPCP daily rainfall data, the current NMSG daily data has more information due to inclusion of local gauge analysed values. In terms of bias and skill scores this dataset is superior to other daily rainfall datasets. In a mean climatological sense and also for anomalous monsoon seasons, this merged satellite gauge data brings out more detailed features of monsoon rainfall. The difference of NMSG and GPCP looks significant. This dataset will be useful to researchers for monsoon intraseasonal studies and monsoon model development research.

In the present study, the assessment of the Community Atmosphere Model (CAM) developed at National Centre for Atmospheric Research (NCAR) for seasonal forecasting of Indian Summer Monsoon (ISM) with different persistent SST is reported. Towards achieving the objective, 30-year model climatology has been generated using observed SST. Upon successful simulation of climatological features of ISM, the model is tested for the simulation of ISM 2011 in forecast mode. Experiments have been conducted in three different time-phases, viz., April, May and June; using different sets of initial conditions (ICs) and the persistent SSTs of the previous months of the time-phases.

The spatial as well as temporal distribution of model simulated rainfall suggest a below normal monsoon condition throughout the season in all the experiments. However, the rainfall anomaly shows some positive signature over north-east part of India in the month of June and August whereas the central Indian landmass had positive anomaly during August and September. The monthly accumulated All-India rainfall (AIR) over land for June to September 2011 are predicted to be 101% (17.6 cm), 86% (24.3 cm), 83% (21.0 cm) and 95% (15.5 cm) of normal AIR, respectively. This makes the seasonal accumulated AIR 78.4 cm which is 11% below the normal rainfall of 87.6 cm. The model prediction for the months of June and July is comparable with the observation; however, the simulation would not be able to capture the high rainfall during August and September. The intention behind this work is to assess the shortcomings in the CAM model prediction, which can later be improved for future monsoon forecast experiments.

We are proposing a statistical technique to analyze the best fit of the histogram of infrared brightness temperature of convective cloud pixels. For this we have utilized the infrared brightness temperatures (IRTB) of Kalpana-1 (8 km resolution) and globally merged infrared brightness temperatures of Climate Prediction Centre NCEP/NWS (4 km resolution, merged from all the available geostationary satellites GOES-8/10, METEOSAT-7/5 and GMS), for both deep convective and non-deep convective (shallow cloud) cases. It is observed that Johnson SB function is the best continuous distribution function in explaining the histogram of infrared brightness temperatures of the convective clouds. The best fit is confirmed by Kolmogorov–Smirnov statistic. Johnson SB’s distribution of histogram of infrared brightness temperatures clearly discriminates the cloud pixels of deep convective and non-deep convective cases. It also captures the asymmetric nature in histogram of infrared brightness temperatures. We also observed that Johnson SB distribution of infrared brightness temperatures for deep convective systems is different in each of the pre-monsoon, monsoon and post-monsoon seasons. And Johnson SB parameters are observed to be best in discriminating the Johnson SB distribution of infrared brightness temperatures of deep convective systems for each season. Due to these properties of Johnson SB function, it can be utilized in the modelling of the histogram of infrared brightness temperature of deep convective and non-deep convective systems. It focuses a new perspective on the infrared brightness temperature that will be helpful in cloud detection, classification and modelling.

Today with increased availability of data of middle atmospheric winds and temperature, modelling of middle atmospheric tides has acquired greater importance. The theory of atmospheric tides has two main parts: (i) Investigation of the sources of periodic excitation, and (ii) calculation of the atmospheric response to the excitation. Other than stratospheric ozone and tropospheric water vapour absorption, the thermal energy available from the absorption in Schumann–Runge (SR) continuum leading to photodissociation of O2 is important energy source for tides in the lower thermosphere. PHODIS radiative transfer model is capable of providing tidal forcing due to combined effect of solar and chemical heating in the wavelength region 116 to 850 nm. In this paper, we present an atmospheric tidal model based on classical tidal theory and the prime objective is to obtain the tidal structure due to conventional ozone and water vapour heating in conjunction with the O2 absorption. Mean wind and dissipation mechanisms are not considered. The present tidal model reveals that the diurnal amplitude peaks in mid to low latitudes, whereas semidiurnal component is stronger at higher latitudes. The semidiurnal tide is about an order of magnitude weaker than the diurnal tide. Also, semidiurnal wave has longer vertical wavelength than diurnal tide. The results of present model are qualitatively in good agreement with the other tidal models, which utilize more sophisticated parameterization. Thus, the salient features of the tidal structure are obtained using basic computations without considering the effects of background winds and dissipation processes. Further refinements to the model can serve as an inexpensive substitute to the presently available tidal models.

In earth observation, the atmospheric particles contaminate severely, through absorption and scattering, the reflected electromagnetic signal from the earth surface. It will be greatly beneficial for land surface characterization if we can remove these atmospheric effects from imagery and retrieve surface reflectance that characterizes the surface properties with the purpose of atmospheric correction. Giving the geometric parameters of the studied image and assessing the parameters describing the state of the atmosphere, it is possible to evaluate the atmospheric reflectance, and upward and downward transmittances which take part in the garbling data obtained from the image. To that end, an atmospheric correction algorithm for high spectral resolution data over land surfaces has been developed. It is designed to obtain the main atmospheric parameters needed in the image correction and the interpretation of optical observations. It also estimates the optical characteristics of the Earth-observation imagery (LANDSAT and SPOT). The physics underlying the problem of solar radiation propagations that takes into account multiple scattering and sphericity of the atmosphere has been treated using Monte Carlo techniques.

In this study, an attempt has been made to develop a decision tree classification (DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source data mining software. The classified image is compared with the image classified using classical ISODATA clustering and Maximum Likelihood Classifier (MLC) algorithms. Classification result based on DTC method provided better visual depiction than results produced by ISODATA clustering or by MLC algorithms. The overall accuracy was found to be 90% (kappa = 0.88) using the DTC, 76.67% (kappa = 0.72) using the Maximum Likelihood and 57.5% (kappa = 0.49) using ISODATA clustering method. Based on the overall accuracy and kappa statistics, DTC was found to be more preferred classification approach than others.

Coal fires in the Jharia coalfield pose a serious threat to India’s vital resource of primary coking coal and the regional environment. In order to undertake effective preventative measures, it is critical to detect the occurrence of subsurface coal fires and to monitor the extent of the existing ones. In this study, Differential Interferometric Synthetic Aperature Radar (DInSAR) technique has been utilized to monitor subsurface coal fires in the Jharia coalfield. Results showed that majority of the coal fire-related subsidence were concentrated on the eastern and western boundaries of the coalfield. The magnitude of subsidence observed was classified into high (10–27.8 mm), low (0–10 mm) and upliftment (−10–0 mm). The results were strongly supported by in situ observations and satellite-based thermal imagery analysis. Major subsidence was observed in the areas with repeated sightings of coal fire. Further, the study highlighted on the capability of the methodology for predicting potential coal fire zones on the basis of land surface subsidence only. The results from this study have major implications for demarcating the hazardous coal fire areas as well as effective implementation of public safety measures.

In the present study, we report initial results on analysis of carbon dioxide (CO2), water vapour (H2O), and energy fluxes (sensible and latent heat flux) over teak mixed deciduous forests of Madhya Pradesh, central India, during winter (November 2011 and January 2012) and summer (February–May 2012) seasons using eddy covariance flux tower datasets. During the study period, continuous fast response measurements of CO2, H2O and heat fluxes above the canopy were carried out at 10 Hz and averaged for 30 minutes. Concurrently, slow response measurements of meteorological parameters are also being carried out. Diurnal and seasonal variations of CO2, H2O and heat fluxes were analysed and correlated with the meteorological variables. The study showed strong influence of leaf off and on scenario on the CO2, H2O and energy fluxes due to prevalence of deciduous vegetation type in the study area. Maximum amount of CO2 was sequestered for photosynthesis during winter (monthly mean of −25 𝜇 mol/m2/s) compared to summer (monthly mean of −2 𝜇 mol/m2/s). Energy flux analysis (weekly mean) showed more energy being portioned into latent heat during winter (668 W/m2) and sensible heat during summer (718 W/m2).

This paper presents a classified desertification hazard map of Zayandeh Rood Basin (Z.R.B) in Iran. There are potential climatic factors, the geographical location and human activities for developing desertification. The present study is based on the preview models and main factors of desertification in the study area. The classification method is based on such main parameters as the characteristic of soil, management and climate. Each parameter includes a sub-indicator with a weighing of between one and two based on the MEDALUS model. DEM, LANDSAT images (ETM+), statistical data and vector map are used as the dataset for classifying the desertification map. The sum of score parameters was estimated by using a weighted average. In this study, Arc GIS 9.3 was used to analyze the vector and raster layer maps as well as a recognition survey of the study area using satellite images. In Z.R.B, the most important factors of desertification hazard are the geographical location of the area, salinization area, elevation, slope, and human activities. Thanks to the classification hazard map, the highest class is located on salinization, low vegetation, high wind erosion, low slope and elevation. Low-class area of the hazard map belongs to the largest part of the basin. An important factor of desertification in the area can be the geographical location of the basin located near the desert area. Using the raster data and GIS software are beneficial for the classification hazard map, especially for large areas.

The stability of some highly weathered soils of the tropics is controlled by their organo-mineral substances. Highly weathered soils from 10 different locations were sampled from their A and B horizons to determine their aggregate stability. The objective of the study was to determine the aggregate stability of the soils and their relationships with geochemical constituents. The major geochemical elements of the soils are quartz and kaolinite, SiO2, Al2O3 and Fe2O3, while the dithionite extractable Fe and Al was greater than their corresponding oxalate and pyrophosphate forms. The mean-weight diameter from dried aggregates (MWDd) and their corresponding wet mean-weight diameter (MWDw) were related significantly (r = 0.64*). The dithionite extracted Al and Fe or the crystalline forms of these elements were outstanding in the stability of the aggregates. However, this did not diminish the influence of SOC reduced to third order level in the stability of the soils. The influence of SOC in these soils, however, indirectly manifested on the role of Fep and Alp in the aggregation of these soils. The crystalline Fe and Al sesquioxides were very prominent in the aggregation and stability of these soils.

The study described here is based on the measurements of soil gas radon–thoron concentrations performed at Dharamsala region of north-west (NW) Himalayas, India. The study area is tectonically and environmentally significant and shows the features of ductile shear zone due to the presence of distinct thrust planes. Solid state nuclear track detectors (LR-115 films) have been used for the soil gas radon–thoron monitoring. Twenty five radon–thoron discriminators with LR-115 films were installed in the borehole of about 50 cm in the study areas. The recorded radon concentration varies from 1593 to 13570 Bq/m3 with an average value of 5292 Bq/m3. The recorded thoron concentration varies from 223 to 2920 Bq/m3 with an average value of 901 Bq/m3. The anomalous value of radon–thoron has been observed near to the faults like main boundary thrust (MBT and MBT2) as well as neotectonic lineaments in the region.

The annual sediment load of a river is generally determined either from direct measurements of the sediment load throughout the year or from any of the many sediment transport equations that are available today. Due to lack of a long-term sediment concentration data, sediment rating curves and flux estimation are the most widely applied. This paper has investigated the abilities of statistical models to improve the accuracy of streamflow–suspended sediment relationships in daily and annual suspended sediment estimation. In this study, a comparison was made between suspended sediment rating curves and artificial neural networks (ANNs) for the El Kebir catchment. Daily water discharge and daily suspended sediment data from the gauging station of Ain Assel, were used as inputs and targets in the models which were based on the cascade-forward and feed-forward back-propagation using Levenberg–Marquardt and Bayesian regularization algorithms. The model results have shown that the ANN models have the highest efficiency to reproduce the daily sediment load and the global annual sediment yields. Our estimation based on the available data indicated that the areas along the El Kebir River have experienced high sediment fluxes that could have obvious impacts on the sediment trapping and siltation in the Mexa reservoir.

The Greater Himalayan region is witnessing a changing rainfall pattern from the last few decades. Low-intensity longer-duration rainfall events have now been replaced with intense and shorter-duration events that are further responsible for the reduced recharging of the spring catchments. Consequently, the natural springs are either drying up or becoming seasonal. Prediction of spring water availability during the recession period is the key to its proper management. The spring discharge-rate can be forecasted by studying its behaviour for the past recession periods. Expressing recession curve in mathematical terms requires its quantitative analyses in priori. It was found that the fitting of recession-curve (of the Ranichauri spring under study) with two exponential components gives accurate results. The maximum value of exponential coefficient (i.e., 0.0206) represents the major contribution to drainage from the spring-catchment’s portion with highest permeability, whereas the minimum value (i.e., 0.0016) represents the major contribution to spring discharge from the portion with lowest permeability. Analyses show that the permeability of the porous medium is responsible for discharge rate and its capacity is responsible for perennial or seasonal behaviour of the spring. Using the mean values of the recession parameters, the master discharge-function of the spring for the recession period is formulated for calculating its discharge-rate during the recession period of any year. Apart from the year 2001, its predictions are in close agreement with the actually monitored data. The efficiency of the formulated master discharge function of the spring for the recession period has been evaluated equal to 0.965 using the Nash–Sutcliffe efficiency criterion.

Fluorescent dissolved organic matter (FDOM) of southwestern Bay of Bengal surface water during southwest monsoon consisted five fluorophores, three humic-like and two protein-like. The humification index (HIX) and humic fluorophores, viz., visible (C), marine (M) and UV (A) humic-likes indicated, better than biogeochemical constituents analyzed, that the northern-half region of the study area which is closer to the head bay (less salinity) is distinctly more terrestrially influenced. Similarly, the southernhalf region (less dissolved oxygen) is indicated as more in situ influenced. This region is enriched with tyrosine protein-like fluorophore (B), an indicator of bacterial metabolism in some of its samples due to upwelled water. Although chlorophyll 𝑎 is less in this (southern) region, the fluorescence based biological index (BIX) which is an index of recent phytoplankton production is about the same in the two regions, and the lower chlorophyll 𝑎 of southern region is attributed to greater grazing pressure. Fluorescence properties, e.g., BIX are more informative about phytoplankton production than chlorophyll 𝑎.

Recent studies using secondary ion mass spectrometry revealed microscale heterogeneity of Sr/Ca and 𝛿18O in shallow-water coral skeletons, i.e., Sr/Ca and 𝛿18O differ significantly between two basic microfeatures of the skeleton: the center of calcification (COC) and surrounding fibrous skeleton (SFS). The COC, in contrast with the SFS, consists of highly irregular crystals intermingled with significant amount of organic matter; therefore, analyzing the SFS only would probably be favourable for paleotemperature reconstruction. Conventional Sr/Ca and 𝛿18O paleothermometers are, however, based on the analysis of the mixture of the COC and SFS, and thus may be significantly affected by the above-mentioned heterogeneity. In this study, I have evaluated the heterogeneity-induced effects on the conventional paleothermometers of Porites skeletons using published Sr/Ca, 𝛿18O and volume-fraction data of the COC and SFS and published observations of seasonal variability of bulk skeletal density. Results indicate that the effects may yield significant or serious errors in paleotemperature reconstruction.

An assemblage of fossil leaves is described from the late Oligocene (Chattian 28.1–23 Ma) sediments of Assam, which was located in a low palaeolatitude (∼10$–15°N) during the period of sedimentation. It includes four new fossil leaves resembling Firmiana and Pterygota of the Malvaceae s.l. and Paranephelium and Sapindus of the Sapindaceae. The present study suggests that the floral migration between India and southeast Asia had occurred after the late Oligocene. Our study is in congruence with the earlier published data suggesting a floral migration had occurred after the complete suturing of two landmasses by the Neogene.

A Lockeia-Protovirgularia ichnofauna representing the Cruziana ichnofacies is reported from a calcareous sandstone horizon of the Callovian Bada Bagh Member, Jaisalmer Formation, Rajasthan. The ichnoassemblage is characterized by Lockeia cunctator, L. siliquaria, Protovirgularia ?bidirectionalis, P. rugosa, Ptychoplasma vagans along with Palaeophycus tubularis, P. striatus, Heliophycus isp. And Lophoctenium isp. Use of numerical analysis yielding length to width ratio, slopes and coefficient of determination helps in confirming identification of ichnospecies of Lockeia. Fine morphological details of Lockeia and Protovirgularia, especially sharp and closely spaced chevrons of Protovirgularia, indicate that the substrate in which they were emplaced was stiff, resistant, dewatered and better consolidated. Therefore, it construes that this ichnoassemblage belonging to the classical Cruziana ichnofacies occurs in stiff softground and not a typical softground. Though the ichnofacies of this calcareous sandstone bed indicates low-medium energy condition under subtidal environment of deposition, underlying strata containing Arenicolites, Skolithos and Curvolithus of Skolithos ichnofacies indicate intertidal sandy shore environment with high energy conditions. Thus, it is concluded that this area was undergoing continuous, gradual deepening. However, the percentage of Thalassinoides, Ophiomorpha and Phycodes in the overlying bed is quite high suggesting an increase in the energy conditions resulting from a probable shallowing.

Climate has played a crucial role in assigning a different kind of topography to Rajasthan and Gujarat since the Cenozoic time. Evidently, three genera, namely, Dipterocarpus Gaert. f., Hopea Roxb. And Shorea Roxb. of the Dipterocarpaceae are described from the Neogene sediments of western India (Rajasthan and Gujarat). These taxa are marked by their complete absence in the region today. The presence of Dipterocarpaceae in western India has been noticed from the Early Eocene up to the Plio-Pleistocene in deep time. The family is usually a dominant component of the humid tropical and subtropical flora of the Indo-Malayan region and its discovery, along with earlier described fossils from western India indicates existence of ancient tropical rain forests in western India. A change in the climate affected warm and humid conditions occurring there during the Cenozoic resulting in arid to semi-arid climate at present which is responsible for the ultimate extinction of Dipterocarpaceae in the region. In addition, the palaeobiogeography of Dipterocarpaceae is reviewed.

Geological storage of CO2 in deep saline formations is increasingly seen as a viable strategy to reduce the release of greenhouse gases into the atmosphere. However, possible leakage of injected CO2 from the storage formation through vertical pathways such as fractures, faults and abandoned wells is a huge challenge for CO2 geological storage projects. Thus, the density-driven fluid flow as a process that can accelerate the phase change of injected CO2 from supercritical phase into aqueous phase is receiving more and more attention. In this paper, we performed higher-resolution reactive transport simulations to investigate the possible density-driven fluid flow process under the ‘real’ condition of CO2 injection and storage. Simulation results indicated that during CO2 injection and geological storage in deep saline formations, the higher-density CO2-saturated aqueous phase within the lower CO2 gas plume migrates downward and moves horizontally along the bottom of the formation, and the higher-density fingers within the upper gas plume propagate downward. These density-driven fluid flow processes can significantly enhance the phase transition of injected CO2 from supercritical phase into aqueous phase, consequently enhancing the effective storage capacity and long-term storage security of injected CO2 in saline formations.

The Narmada–Son lineament (NSL) is one of the most prominent tectonic features which divides the Indian peninsula into two subcontinents, northern and southern India since Precambrian times. The area is seismically active and geologically complex with different geological formations. Magnetic data divides the area into two parts and more prominent magnetic highs are observed near Tikwa, Mau and Amarpur regions with 800, 600 and 400 nT, respectively due to the presence of the crystalline basement rock. Tectonic resettlement and lithological changes causes upwarpment of Mahakoshal rocks. In the present study, magnetic data interpretation is carried out for locating depth of causative body and delineating structural fault/dyke boundaries using Euler deconvolution technique. Most of the faults are oriented in the ENE–WSW direction; however, few more faults are identified which are oriented in the SE to NW direction. These fault patterns suggest that the area is exaggerated by tectonic turmoil and distressed both sedimentary to basement rocks isolating the area into numerous faulted blocks. The maximum depths (&lt; 4.5 km) observed at Katni and Umaria area and moderate depths (between 4.0 and 4.5 km) observed towards east of Katni, Ramnagar, Burwa and east of Umaria and Sarna area.

This paper aims to study the dispersion of torsional surface waves in a crustal layer being sandwiched between a rigid boundary plane and a sandy mantle. In the mantle, rigidity and initial stress vary linearly while density remains constant. Dispersion relation has been deduced in a closed form by means of variable separable method in the form of Whittaker function. The velocity equation for isotropic layer over a homogeneous half-space has been obtained which coincides with the standard result of Love wave under the effect of rigid boundary.

This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude.